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\begin{document}

\title{Literature review for Topics}
\author{%
Benyuan~Liu$^{1,*}$%
\thanks{\textit{Asterisk indicates corresponding author.}}%
\thanks{$^1$B. Liu is with the Department of Biomedical Engineering,
Fourth Military Medical University, Xi'an 710032, China. (e-mail: byliu@fmmu.edu.cn)}
\thanks{Manuscript received \today{}.}%
}

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% {Liu \MakeLowercase{\textit{et al.}}: Quantized Compressive Sensing}

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%------------------------------------------------------------------------------

\begin{abstract}
\boldmath
In low-power wireless telemonitoring,
physiological signals must be compressed before transmission to extend battery life.
In this paper, we propose a two-stage data compressor based on quantized compressive sensing (QCS),
where signals are firstly compressed by compressive sensing with a 50\% compression ratio
and then quantized with 2-bit per measurement. 该模板支持中文摘要，采用宋体中宋作为加黑字体。
\end{abstract}
\begin{IEEEkeywords}
    Quantized Compressive Sensing, Block Sparse Bayesian Learning, Data Compression, Telemonitoring
\end{IEEEkeywords}

%------------------------------------------------------------------------------

%---- 1. intro
\section{Introduction}

In wireless health monitoring, hybrid types of physiological signals are collected
from on-body sensors, then these data are transmitted to nearby smart phones or a data central via wireless networks.
该模板支持中文，便于首先进行文献回顾、提出方法、设计实验，最后汇总为英文论文。

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The wearable devices are usually battery powered. To reduce the power consumption and extend battery life,
data must be compressed before transmission. liu et, al. \cite{liu2013energy}.

%----
\section*{Acknowledgment}
The authors thank Dr. Hongqi Fan, Dr. Xiaoyi Pan for valuable discussions.
We would also like to thank anonymous reviewers for providing insightful comments.

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% that's all folks, happy reading!
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